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- Title
Nonparametric universal copula modeling.
- Authors
Mukhopadhyay, Subhadeep; Parzen, Emanuel
- Abstract
To handle the ubiquitous problem of "dependence learning," copulas are quickly becoming a pervasive tool across a wide range of data‐driven disciplines encompassing neuroscience, finance, econometrics, genomics, social science, machine learning, healthcare, and many more. At the same time, despite their practical value, the empirical methods of "learning copula from data" have been unsystematic with full of case‐specific recipes. Taking inspiration from modern LP‐nonparametrics, this paper presents a modest contribution to the need for a more unified and structured approach of copula modeling that is simultaneously valid for arbitrary combinations of continuous and discrete variables.
- Subjects
STATISTICAL learning; MACHINE learning; EMPIRICAL research; GENOMICS; ECONOMETRICS; FARM finance; NEUROSCIENCES
- Publication
Applied Stochastic Models in Business & Industry, 2020, Vol 36, Issue 1, p77
- ISSN
1524-1904
- Publication type
Article
- DOI
10.1002/asmb.2503